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Practical Data Analysis

You're reading from   Practical Data Analysis For small businesses, analyzing the information contained in their data using open source technology could be game-changing. All you need is some basic programming and mathematical skills to do just that.

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Product type Paperback
Published in Oct 2013
Publisher Packt
ISBN-13 9781783280995
Length 360 pages
Edition 1st Edition
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Author (1):
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Hector Cuesta Hector Cuesta
Author Profile Icon Hector Cuesta
Hector Cuesta
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Toc

Table of Contents (24) Chapters Close

Practical Data Analysis
Credits
Foreword
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started FREE CHAPTER 2. Working with Data 3. Data Visualization 4. Text Classification 5. Similarity-based Image Retrieval 6. Simulation of Stock Prices 7. Predicting Gold Prices 8. Working with Support Vector Machines 9. Modeling Infectious Disease with Cellular Automata 10. Working with Social Graphs 11. Sentiment Analysis of Twitter Data 12. Data Processing and Aggregation with MongoDB 13. Working with MapReduce 14. Online Data Analysis with IPython and Wakari Setting Up the Infrastructure Index

Degree distribution


The degree of a node is the number of connections (links) with other nodes. In the case of directed graphs, each node has two degrees: the out degree and the in degree. In the undirected graph, the relationship is mutual, so we just have a single degree for each node. In the following code snippet we get the source node and target node references from the file links.csv. Then we create a single list to merge the two lists (target and source). Finally, we get a dictionary (dic) of how many times each node appears in the list and we plot the result in a bar chart using matplotlib.

The file links.csv will look as follows:

edgedef>node1 VARCHAR,node2 VARCHAR
23917067,35702006
23917067,629395837
23917067,747343482
23917067,755605075
23917067,1186286815
.  .  .

The complete code snippet looks as follows:

import numpy as np
import matplotlib.pyplot as plt

links = np.genfromtxt("links.csv",
                       dtype=str,
                       delimiter=',',
           ...
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